Method, System, and Program Product for Migrating Data from One Data Base Management System to Another Data Base Management System

Abstract

A method, system, and program product to migrate data from a data table in a data source to a data table in a target. This is done initiating processes, typically two parallel batch processes, with one of the processes unloading the data from the data source and uploading the data into the data target, and the other of the processes loading the source data into a database at the data target. Preferably the processes are parallel batch process. The processes are initiated from the data target.

Description

[0001]The present application is a Continuation of U.S. application Ser. No. 10 / 102,385, filed Mar. 18, 2002. The entire disclosure which is hereby incorporated by reference.FIELD OF THE INVENTION[0002]The method, system, and program product described herein are used for migrating large volumes of data from a data source to a data target, and include organizing and inter-relating data or files, e.g., providing an association between different schema and metadata, with, for example, data structure conversion, for optimization of database and file migration and storage, and for data compatibility between different or multiple databases.BACKGROUND OF THE INVENTION[0003]Current approaches for migrating large quantities (for example, gigabytes) of data from relational data stores on various database platform sources, such as Windows NT, AIX, Linux, Solaris, and other database platforms, to databases on disparate target platforms, such as IBM “DB2 UDB for OS / 390,” do not perform well and ...

AI Technical Summary

Benefits of technology

[0004]The method, system, and program product described herein use bulk data loading interfaces to migrate the source data into a target data store. The method, system, and program product of the invention eliminate the use of intermediate data stores on the target system, execute the extraction of the data from the source data store and the placement of the data into the target data store in parallel, minimize the datatype conversions that are performed, transfer the data in large blocks, and perform the migration in a fully automated fashion. This approach reduces the complexity and the elapsed time required for performing these types of data migrations.

[0006]The unload of a particular database table at the source and the subsequent load of the database table at the target execute in parallel. The method, system, and program product allow for the parallel unload and load of the database table by eliminating the use of intermediate data files between the two batch processes. Data flows directly from the unload batch process to the load batch process, thereby reducing both the elapsed time and the complexity of the data migration. The method, system, and program product of the invention also permit a write of the data being migrated to disk on the target system, in parallel with the load of the database table. This copy allows the load of the data to be restartable from the last internal load utility checkpoint in the event of a load utility failure.

[0008]Overall, according to the method, system, and program product of the invention, the complexity of generating SQL view mapping structures, generating batch jobs and coordinating and executing the migration task is reduced due to automated processes and tools.

Problems solved by technology

Current approaches for migrating large quantities (for example, gigabytes) of data from relational data stores on various database platform sources, such as Windows NT, AIX, Linux, Solaris, and other database platforms, to databases on disparate target platforms, such as IBM “DB2 UDB for OS / 390,” do not perform well and are overly complex.

Poor performance and complexity can be attributed to a variety of causes including (1) using SQL in a multi-step process to both extract the data from the source data store and place the data into the target data store, (2) storing the data in one or more intermediate data stores (e.g. local file systems) during data migration, (3) performing multiple datatype conversions as the data is moved between environments, (4) using less efficient and inefficient data transfer mechanisms, and (5) using data migration processes that are not fully automated.

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